In this two-part blog series, we explore a robust end-to-end architecture powered by modern deep learning techniques and built on Microsoft Azure to implement an automated service ticket routing solution.
In the first part, we discuss key architectural details highlighting the usage of serverless and PaaS services in Microsoft Azure that allow the rapid implementation of the solution presented here or a similar one.
In the upcoming second part of this series we get into the details of designing and developing the machine learning model used for the categorization of service tickets, using advanced deep learning and NLP (Natural Language Processing) techniques on the Azure Machine Learning service platform.
Why service ticket routing
Here we consider service ticket routing as the process of delivering a customer service ticket to the right recipient in a customer service organization.
As businesses become even more customer centric, an accurate and agile service ticket routing has become increasingly important as the foundation to provide an efficient and enjoyable communication channel for customers.
Moreover, modern businesses rely less on traditional human interactions in their Help Desk systems in favor of more automated channels such as bots, mobile apps, or text messaging.
In this context, being able to automatically route support tickets, customer inquiries, or complaints to the right channel can make a huge impact in customer satisfaction and beyond that, such as increased operational efficiency and sales.
The challenges of service ticket routing
Traditionally, this process is initiated by a user interacting with a product or service provider through one of its available customer interaction channels.
This will then trigger a service ticket creation in a Help Desk or Service Management solution.
Finally, this ticket ideally needs to be delivered to the right recipient, which can be, for example, the product or service support organization, a sales team, or a service or product specialist.
Routing those tickets manually is usually a slow, inefficient, error-prone and non-scalable process. If a ticket is not routed to the right recipient in the first place, this could potentially compromise a business in several ways: from disrupted service-level agreements to negative brand reputation. Here is where a machine learning enabled solution can help.
Automated ticket routing enabled by Machine Learning
Unless the service ticket is created or processed by a knowledgeable human capable of correctly identifying the right recipient for it, an automated way to categorize the information that comprises the ticket is needed in order to implement automated routing.
A critical part of this process is to rely on NLP (Natural Language Processing) and Machine Learning techniques to automatically categorize text information extracted from the tickets, in order to match a given category with the correct recipient.
Proposed end-to-end solution architecture on Azure
Here we discuss, at a high level, the solution architecture proposed on Microsoft Azure that supports a generic workflow for automated ticket routing.
We considered the following architecture principles when designing it:
Considering these principles, a best practice is to use Serverless and PaaS solutions on Azure to design the proposed architecture.
The core objective of this architecture is to support the data ingestion, integration, and processing workflows needed to develop and implement the machine learning model for text categorization, as well as triggering the automated ticket routing in the service ticket management platform.
In the diagram below we have a high-level overview of this solution architecture:
Fig. 1: high-level overview of the proposed solution architecture
There are only two integration points in the proposed architecture:
This architecture supports two distinct data ingestion workflow patterns:
Following each of the two data ingestion workflows described above, we also have two data processing workflows:
How a typical workflow is processed in the proposed solution
Here we describe in more detail how the typical workflows for model development, training, and scoring are performed following the numeric labels depicted in the diagram below:
Fig. 2: workflows for model development, training, and scoring
Training and deploying the machine learning model:
Using the Machine Learning model to drive the routing of new service tickets:
The modular and composable architectural approach, coupled with serverless and PaaS services and platforms available on Microsoft Azure, allows organizations of any size and budget to benefit from the transformational power provided by cloud-based, AI-driven solutions.
Here we showed an example of a solution architecture implemented to address the problem of automated service ticket routing, with recommended patterns for data ingestion, data preparation, machine learning model development and operationalization on Microsoft Azure. To learn more about Microsoft-proposed solution architectures on Azure, please refer to this documentation.
Stay tuned for the second part of this series, where we explore the details of the machine learning model and how to develop and operationalize it using the Azure Machine Learning service platform.
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